Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data
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Abstract:
Background High-throughput gene expression data can predict gene function through the “guilt by association” principle: coexpressed genes are likely to be functionally associated. Methodology/Principal Findings We analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. Conclusions/Significance We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis.Keywords:
Candidate gene
Gene Annotation
Functional Genomics
Functional Genomics
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Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.
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Gene chip analysis
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The number of cardiovascular publications featuring gene expression profiling technologies is growing rapidly. This article introduces four profiling techniques; serial analysis of gene expression, differential display, subtractive hybridisation and DNA microarrays. Illustrations of their application towards cardiovascular research are given and their potential for gene discovery and improving our understanding of gene function is discussed.
Profiling (computer programming)
Suppression subtractive hybridization
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A bstract : With completion of the human genome sequence, it is now possible to study the expression of the entire human gene vcomplement of ∼30,000‐35,000 genes. To accomplish this goal, microarrays have become the leading methodology for the analysis of global gene expression. Improvements in technology have increased the sensitivity of microarrays to the point where it is feasible to study gene expression in a small number of cells and even at the single cell level . A summary of developments in the area of expression profiling in single cells will be described, and the rationale for these types of studies will be presented. In addition, from a biologist's point of view, some bioinformatic challenges of expression analysis of single cells will be discussed.
Single-Cell Analysis
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Microarray databases
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Although microarray analysis is a highly promising technology in the genome era, its application for gene expression profiling to characterize various phenomes, including genetic phenotypes, diseases, responses to chemicals and clinical annotations, is far from being a real use. One of the obstacles is the quality of the data, which needs to be enough to be able to solely use microarrays for these purposes. For this, selecting a set of genes as a molecular signature, based on transcriptomics, proteomics or metabolomics, and the use of the selected set of genes in focused microarrays has great merits. Here, we summarize how sets of genes were selected, what types of genes were used and what kind of statistics will be needed for focused microarrays, to distinguish them from genome-wide microarrays and to explain why focused microarray analysis is advantageous in gene expression profiling. Keywords: cDNA array, genome-wide microarray, Replicate Assays, EUROSTERONE microarray, Signaling Pathways
Gene chip analysis
Microarray databases
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